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Featured in Development

As part of our core values of sharing knowledge, the InfoQ editors were keen to capture and share our book and article recommendations for 2018, so that others can benefit from this too. In this second part we are sharing the final batch of recommendations

Featured in Architecture & Design

Tanya Reilly discusses her research into how the fire code evolved in New York and draws on some of the parallels she sees in software. Along the way, she discusses what it means to be an SRE, what effective aspects of the role might look like, and her opinions on what we as an industry should be doing to prevent disasters.

Featured in Culture & Methods

Mik Kersten has published a book, Project to Product, in which he describes a framework for delivering products in the age of software. Drawing on research and experience with many organisations across a wide range of industries, he presents the Flow Framework™ as a way for organisations to adapt their product delivery to the speed of the market.

Featured in DevOps

The fact that machine learning development focuses on hyperparameter tuning and data pipelines does not mean that we need to reinvent the wheel or look for a completely new way. According to Thiago de Faria, DevOps lays a strong foundation: culture change to support experimentation, continuous evaluation, sharing, abstraction layers, observability, and working in products and services.

Baidu, the Chinese Internet giant, has released ApolloScape, a massive dataset for autonomous vehicle simulation and machine learning.

ApolloScape is an order of magnitude bigger and more complex than existing similar datasets such as Kitti and CityScapes. ApolloScape offers 10 times more high-resolution images with pixel-by-pixel annotations, and includes 26 different recognizable objects such as cars, bicycles, pedestrians and buildings. The dataset offers several levels of scene complexity with increasing number of pedestrians and vehicles, up to 100 vehicles in a given scene, as well as a wider set of challenging environments such as heavy weather or extreme lighting conditions. The ApolloScape dataset is a work in progress, and this release corresponds to the first subset, which contains 144k image frames.

This dataset will be used to boost research on automated-learning tasks such as finding the roads (Drivable Area Segmentation), detecting the objects (Road Object Detection), allowing model generalization for different locations or weather conditions (Domain Adaptation of Semantic Segmentation) and tracking moving objects (Instance-level Video Movable Object Segmentation).

These research tasks make up the Workshop on Autonomous Driving (WAD) Challenge sponsored by Baidu and taking place next June during CVPR 2018, the IEEE International Conference on Computer Vision and Pattern Recognition. The WAD challenge regroups researchers and engineers across academia and industries to discuss computer vision applications in autonomous driving.

According to ArsTechnica, Waymo, the self-driving unit of Google parent company Alphabet, is currently leading the global innovation in autonomous vehicles along with GM, while Baidu is for the time being viewed more as a contender in the automated driving sector. Opening up the ApolloScape dataset could be interpreted as a move by Baidu to weaken Google's data advantage and increase its own relative position in the industry.

To that effect, Baidu further announced it has joined the Berkeley DeepDrive (BDD) Industry Consortium, a top-tier research alliance which includes Ford, NVIDIA, Qualcomm, and General Motors. BDD focuses on innovations in deep reinforcement learning, cross-modal transfer learning applied to autonomous driving.

Baidu has also partnered with Udacity, an online data-science education website, to launch on online course titled Intro to Apollo which is part of Udacity’s nano degree on self-driving cars. The course start date has not yet been set.